The present invention, in some embodiments thereof, relates to data and, more specifically, but not exclusively, to identification of solution points in an objective space.
Multi-objective Optimization, often referred to as Vector Optimization, strives at simultaneously optimizing multiple conflicting objective functions. Unlike single-objective optimization, which returns a single optimal solution that minimizes or maximizes the objective function, multi-objective optimization returns an optimal set, called the Pareto Frontier of the problem. The latter comprises the entire spectrum of solutions that satisfy a Pareto optimality condition so that: (1) every solution within the optimal set cannot be dominated by another solution (i.e., inferior in all objective function values) and/or (2) no solution is better than another in all the objective values. Thus, decision makers that are imposed with multi-objective problems and consequently choose to employ multi-objective optimization solvers are provided with an optimal set of solutions of which they eventually need to select a single solution. Since all the given solutions within the Frontier are optimal, the narrowing-down process is subjective.